2024
DOI: 10.1088/1757-899x/1317/1/012006
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Forecasting of the Event-driven Processes Using LSTM Network in the Context of Time of Arrival of On-demand City Transport

Angel Marchev,
Boyan Lomev

Abstract: The precise forecasting of bus arrival times is an important element of implementing on demand city transport. This research uses of Long Short Term Memory (LSTM) networks for predicting bus arrival times in Sofia, Bulgaria. We evaluate the LSTM model against advanced models such as ARIMAX, VARX SARIMAX with Fourier terms Vector Autoregression, Bayesian Fourier models and Backpropagation Neural Networks using Root Mean Squared Error (RMSE) as the performance measure. The results points towards LSTM being bette… Show more

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